Calculate break-even from requests, not a blended guess
Self-hosting wins on cost only if it can serve your actual workload at lower all-in cost and acceptable quality. The generated chart and table show maintained API rates, not a promise that either model fits a particular GPU. Check memory, context length, quantization, license, and serving configuration before selecting hardware.
For a representative request with 2,000 input tokens and 500 output tokens, use the displayed rates:
API cost per request = (2,000 × input rate + 500 × output rate) / 1,000,000.
Then calculate:
Break-even accepted requests/month = all-in monthly hosting cost / API cost per accepted request.
Use accepted requests on both sides. If an API route needs retries, include those tokens; if the local route needs repair calls, premium fallbacks, or human review, include those costs too. A benchmark completion that fails your validator is not a successful request.
For a quick monthly scenario, enter 10 million input tokens and 2.5 million output tokens in the inline calculator. Compare its API result with a current GPU quote plus operations. This is an illustrative workload, not a forecast of your traffic. The full token calculator supports additional cache scenarios.
| Cost item | Managed API | Rented GPU |
|---|---|---|
| Quiet periods | Usually no inference token charge | Reserved capacity may still bill |
| Bursts | Subject to vendor limits | Subject to measured serving capacity |
| Operations | App integration and monitoring | Serving, patching, monitoring, and recovery |
| Quality checks | Measure accepted results and retries | Repeat the same checks for your configuration |
| Pricing data: How we verify → | ||
See Groq pricing for the API catalog and Together AI pricing for another managed-hosting comparison. Vendor-specific execution, model versions, and limits can differ even when model names look similar.
Check capacity and the complete hosting bill
For an always-on hourly rental, estimate hourly quote × provisioned hours, then add storage, network charges, orchestration, monitoring, engineering time, and fallback inference. Do not assume a signup credit is a recurring saving. Serverless GPU billing can also include startup, minimum billing, or idle periods; use the actual terms of your quote.
Next, run a representative load test. Capture input length, generated output, concurrency, latency targets, memory use, and accepted requests per hour. Prefill and decoding have different costs; a single headline tokens-per-second figure cannot represent every prompt mix.
Your capacity must clear the economic break-even and serve peak demand within the latency target. If the calculated break-even exceeds the machine’s monthly capacity, that configuration cannot win under those assumptions. If it fits only at continuous full utilization, test a realistic idle-time and headroom scenario before committing.
A local deployment may still be justified by data control or customization even when inference is not cheaper. Document that requirement separately instead of inventing token savings. Our local AI vs API guide covers ownership and personal subscriptions; this page focuses on rented infrastructure.
Choose a route and validate it before committing
- Keep the API when demand is irregular, operational capacity is limited, or a managed route meets the quality and reliability target at lower total cost.
- Test rented GPUs when traffic is steady, the model fits the hardware, and a load test supports the capacity and cost forecast.
- Use a hybrid when a local or rented model handles predictable work but difficult requests need a managed fallback. Count that fallback traffic in the budget.
For a rental quote, compare RunPod GPU availability and billing with Vultr Cloud GPU. For a managed alternative, check Novita’s current model catalog; confirm the exact endpoint rather than assuming price or feature parity.
Affiliate disclosure: this article contains affiliate links. We may earn a commission at no extra cost to you; partner status does not determine the cost comparison.
Sources: our maintained pricing dataset and Groq’s official pricing. The generated modules use the dataset bundled with this build; they are not a fresh GPU quote. Verify provider terms and availability before deployment.